Clark Fitzgerald clarkfitzg

Then make it locatable on your system PATH, something like:

$ ln -s /home/clark/dev/ripser/ripser /usr/local/bin/ripser

After this your system should be able to find ripser:

$ which ripser

	# Clark 9/27/13
	#
	# This script graphs the conversion rate data
	# You'll need to change the file.path parameter to the location of your data.

	file.path <- "/home/shared/barug_oct13/hourly_rate.csv"

	require(ggplot2)
	require(grid)

	# Clark Fitzgerald 23 Sep 13
	#
	# This script gets current exchange rates and writes to csv file.

	csv.path <- "/home/shared/barug_oct13/hourly_rate.csv"

	library("XML")

	# We'll compare the conversion rate from US to South Korea.
	country <- "korea"

	> library(ddR)

	Welcome to 'ddR' (Distributed Data-structures in R)!
	For more information, visit: https://github.com/vertica/ddR

	Attaching package: ‘ddR’

	The following objects are masked from ‘package:base’:

	cbind, rbind

	# Mon Jul 18 08:08:09 PDT 2016

	# Goal: Store arbitrary objects in DataFrames as bytes to make dapply more
	# general
	#
	# Inefficient- this uses CLOB rather than BLOB
	# Comments throughout this question are helpful
	# http://stackoverflow.com/questions/5950084/how-to-handle-binary-strings-in-r

	library(SparkR)

	# A very simple parallel program
	#
	# We specify the probability that each individual
	# votes for a candidate, and then simulate the counts
	# for n such voters.
	#
	# count_votes and count_votes_slow are the functions
	# to parallelize. Typically each run will take some
	# time to complete.
	#

	# Checking to see how this saves. What will it look like?
	# Not sure
	x <- 1:10

	x = 1:3
	y = 11:13
	# Given vectors x, y, what's the cleanest general way to make z?
	# z = c(1, 11, 2, 12, 3, 13)

	shuffle = function(x, y)
	{
	as.vector(mapply(c, x, y))
	}

	"""
	http://stackoverflow.com/questions/41886507/data-table-faster-row-wise-recursive-update-within-group/41891693#41891693

	require(data.table) # v1.10.0
	n_smpl = 1e6
	ni = 5
	id = rep(1:n_smpl, each = ni)
	smpl = data.table(id)
	smpl[, time := 1:.N, by = id]
	a_init = 1; b_init = 1